spynnaker.pyNN.models.neuron.builds package¶
Module contents¶
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class
spynnaker.pyNN.models.neuron.builds.
EIFConductanceAlphaPopulation
(**kwargs)[source]¶ Bases:
object
Exponential integrate and fire neuron with spike triggered and sub-threshold adaptation currents (isfa, ista reps.)
Warning
Not currently supported by the tool chain.
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default_initial_values
= {'gsyn_exc': 0.0, 'gsyn_inh': 0.0, 'v': -70.6, 'w': 0.0}¶
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default_parameters
= {'a': 4.0, 'b': 0.0805, 'cm': 0.281, 'delta_T': 2.0, 'e_rev_E': 0.0, 'e_rev_I': -80.0, 'i_offset': 0.0, 'tau_m': 9.3667, 'tau_refrac': 0.1, 'tau_syn_E': 5.0, 'tau_syn_I': 0.5, 'tau_w': 144.0, 'v_reset': -70.6, 'v_rest': -70.6, 'v_spike': -40.0, 'v_thresh': -50.4}¶
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class
spynnaker.pyNN.models.neuron.builds.
HHCondExp
(**kwargs)[source]¶ Bases:
object
Single-compartment Hodgkin-Huxley model with exponentially decaying current input.
Warning
Not currently supported by the tool chain.
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default_initial_values
= {'gsyn_exc': 0.0, 'gsyn_inh': 0.0, 'v': -65.0}¶
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default_parameters
= {'cm': 0.2, 'e_rev_E': 0.0, 'e_rev_I': -80, 'e_rev_K': -90.0, 'e_rev_Na': 50.0, 'e_rev_leak': -65.0, 'g_leak': 0.01, 'gbar_K': 6.0, 'gbar_Na': 20.0, 'i_offset': 0.0, 'tau_syn_E': 0.2, 'tau_syn_I': 2.0, 'v_offset': -63}¶
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class
spynnaker.pyNN.models.neuron.builds.
IFCondAlpha
(**kwargs)[source]¶ Bases:
object
Leaky integrate and fire neuron with an alpha-shaped current input.
Warning
Not currently supported by the tool chain.
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default_initial_values
= {'gsyn_exc': 0.0, 'gsyn_inh': 0.0, 'v': -65.0}¶
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default_parameters
= {'cm': 1.0, 'e_rev_E': 0.0, 'e_rev_I': -70.0, 'i_offset': 0, 'tau_m': 20, 'tau_refrac': 0.1, 'tau_syn_E': 0.3, 'tau_syn_I': 0.5, 'v_reset': -65.0, 'v_rest': -65.0, 'v_thresh': -50.0}¶
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class
spynnaker.pyNN.models.neuron.builds.
IFCondExpBase
(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard
Leaky integrate and fire neuron with an exponentially decaying conductance input.
Parameters: - tau_m (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_m\)
- cm (float, iterable(float), RandomDistribution or (mapping) function) – \(C_m\)
- v_rest (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{rest}\)
- v_reset (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{reset}\)
- v_thresh (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{thresh}\)
- tau_syn_E (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_e\)
- tau_syn_I (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_i\)
- tau_refrac (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{refrac}\)
- i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)
- e_rev_E (float, iterable(float), RandomDistribution or (mapping) function) – \(E^{rev}_e\)
- e_rev_I (float, iterable(float), RandomDistribution or (mapping) function) – \(E^{rev}_i\)
- v (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{init}\)
- isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_e\)
- isyn_inh (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_i\)
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class
spynnaker.pyNN.models.neuron.builds.
IFCurrAlpha
(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard
Leaky integrate and fire neuron with an alpha-shaped current-based input.
Parameters: - tau_m (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_m\)
- cm (float, iterable(float), RandomDistribution or (mapping) function) – \(C_m\)
- v_rest (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{rest}\)
- v_reset (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{reset}\)
- v_thresh (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{thresh}\)
- tau_syn_E (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_e\)
- tau_syn_I (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_i\)
- tau_refrac (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{refrac}\)
- i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)
- v (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{init}\)
- exc_response (float, iterable(float), RandomDistribution or (mapping) function) – \(response^\mathrm{linear}_e\)
- exc_exp_response (float, iterable(float), RandomDistribution or (mapping) function) – \(response^\mathrm{exponential}_e\)
- inh_response (float, iterable(float), RandomDistribution or (mapping) function) – \(response^\mathrm{linear}_i\)
- inh_exp_response (float, iterable(float), RandomDistribution or (mapping) function) – \(response^\mathrm{exponential}_i\)
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class
spynnaker.pyNN.models.neuron.builds.
IFCurrDualExpBase
(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard
Leaky integrate and fire neuron with two exponentially decaying excitatory current inputs, and one exponentially decaying inhibitory current input.
Parameters: - tau_m (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_m\)
- cm (float, iterable(float), RandomDistribution or (mapping) function) – \(C_m\)
- v_rest (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{rest}\)
- v_reset (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{reset}\)
- v_thresh (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{thresh}\)
- tau_syn_E (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_{e_1}\)
- tau_syn_E2 (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_{e_2}\)
- tau_syn_I (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_i\)
- tau_refrac (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{refrac}\)
- i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)
- v (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{init}\)
- isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_{e_1}\)
- isyn_inh (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_i\)
- isyn_exc2 (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_{e_2}\)
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class
spynnaker.pyNN.models.neuron.builds.
IFCurrExpBase
(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard
Leaky integrate and fire neuron with an exponentially decaying current input.
Parameters: - tau_m (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_m\)
- cm (float, iterable(float), RandomDistribution or (mapping) function) – \(C_m\)
- v_rest (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{rest}\)
- v_reset (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{reset}\)
- v_thresh (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{thresh}\)
- tau_syn_E (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_e\)
- tau_syn_I (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_i\)
- tau_refrac (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{refrac}\)
- i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)
- v (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{init}\)
- isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_e\)
- isyn_inh (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_i\)
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class
spynnaker.pyNN.models.neuron.builds.
IFFacetsConductancePopulation
(**kwargs)[source]¶ Bases:
object
Leaky integrate and fire neuron with conductance-based synapses and fixed threshold as it is resembled by the FACETS Hardware Stage 1
Warning
Not currently supported by the tool chain.
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default_initial_values
= {'v': -65.0}¶
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default_parameters
= {'e_rev_I': -80, 'g_leak': 40.0, 'tau_syn_E': 30.0, 'tau_syn_I': 30.0, 'v_reset': -80.0, 'v_rest': -65.0, 'v_thresh': -55.0}¶
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class
spynnaker.pyNN.models.neuron.builds.
IzkCondExpBase
(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard
Izhikevich neuron model with conductance inputs.
Parameters: - a (float, iterable(float), RandomDistribution or (mapping) function) – \(a\)
- b (float, iterable(float), RandomDistribution or (mapping) function) – \(b\)
- c (float, iterable(float), RandomDistribution or (mapping) function) – \(c\)
- d (float, iterable(float), RandomDistribution or (mapping) function) – \(d\)
- i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)
- u (float, iterable(float), RandomDistribution or (mapping) function) – \(u_{init} = \delta V_{init}\)
- v (float, iterable(float), RandomDistribution or (mapping) function) – \(v_{init} = V_{init}\)
- tau_syn_E (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_e\)
- tau_syn_I (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_i\)
- e_rev_E (float, iterable(float), RandomDistribution or (mapping) function) – \(E^{rev}_e\)
- e_rev_I (float, iterable(float), RandomDistribution or (mapping) function) – \(E^{rev}_i\)
- isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_e\)
- isyn_inh (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_i\)
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class
spynnaker.pyNN.models.neuron.builds.
IzkCurrExpBase
(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard
Izhikevich neuron model with current inputs.
Parameters: - a (float, iterable(float), RandomDistribution or (mapping) function) – \(a\)
- b (float, iterable(float), RandomDistribution or (mapping) function) – \(b\)
- c (float, iterable(float), RandomDistribution or (mapping) function) – \(c\)
- d (float, iterable(float), RandomDistribution or (mapping) function) – \(d\)
- i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)
- u (float, iterable(float), RandomDistribution or (mapping) function) – \(u_{init} = \delta V_{init}\)
- v (float, iterable(float), RandomDistribution or (mapping) function) – \(v_{init} = V_{init}\)
- tau_syn_E (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_e\)
- tau_syn_I (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_i\)
- isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_e\)
- isyn_inh (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_i\)
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class
spynnaker.pyNN.models.neuron.builds.
IFCondExpStoc
(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard
Leaky integrate and fire neuron with a stochastic threshold.
Habenschuss S, Jonke Z, Maass W. Stochastic computations in cortical microcircuit models. PLoS Computational Biology. 2013;9(11):e1003311. doi:10.1371/journal.pcbi.1003311
Parameters: - tau_m (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_m\)
- cm (float, iterable(float), RandomDistribution or (mapping) function) – \(C_m\)
- v_rest (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{rest}\)
- v_reset (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{reset}\)
- v_thresh (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{thresh}\)
- tau_syn_E (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_e\)
- tau_syn_I (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_i\)
- tau_refrac (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{refrac}\)
- i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)
- e_rev_E (float, iterable(float), RandomDistribution or (mapping) function) – \(E^{rev}_e\)
- e_rev_I (float, iterable(float), RandomDistribution or (mapping) function) – \(E^{rev}_i\)
- du_th (float, iterable(float), RandomDistribution or (mapping) function) – \(du_{thresh}\)
- tau_th (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{thresh}\)
- v (Float, float, iterable(float), RandomDistribution or (mapping) function) – \(V_{init}\)
- isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_e\)
- isyn_inh (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_i\)
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class
spynnaker.pyNN.models.neuron.builds.
IFCurrDelta
(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard
Leaky integrate and fire neuron with an instantaneous current input.
Parameters: - tau_m (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_m\)
- cm (float, iterable(float), RandomDistribution or (mapping) function) – \(C_m\)
- v_rest (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{rest}\)
- v_reset (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{reset}\)
- v_thresh (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{thresh}\)
- tau_refrac (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{refrac}\)
- i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)
- v (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{init}\)
- isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_e\)
- isyn_inh – \(I^{syn}_i\)
Type: isyn_inh: float, iterable(float), RandomDistribution or (mapping) function
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class
spynnaker.pyNN.models.neuron.builds.
IFCurrExpCa2Adaptive
(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard
Model from Liu, Y. H., & Wang, X. J. (2001). Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron. Journal of Computational Neuroscience, 10(1), 25-45. doi:10.1023/A:1008916026143
Parameters: - tau_m (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_m\)
- cm (float, iterable(float), RandomDistribution or (mapping) function) – \(C_m\)
- v_rest (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{rest}\)
- v_reset (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{reset}\)
- v_thresh (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{thresh}\)
- tau_syn_E (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_e\)
- tau_syn_I (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_i\)
- tau_refrac (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{refrac}\)
- i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)
- tau_ca2 (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{\mathrm{Ca}^{+2}}\)
- i_ca2 (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{\mathrm{Ca}^{+2}}\)
- i_alpha (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_\alpha\)
- v (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{init}\)
- isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_e\)
- isyn_inh (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_i\)
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class
spynnaker.pyNN.models.neuron.builds.
IFCurrExpSEMDBase
(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard
Leaky integrate and fire neuron with an exponentially decaying current input, where the excitatory input depends upon the inhibitory input (see https://www.cit-ec.de/en/nbs/spiking-insect-vision)
Parameters: - tau_m (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_m\)
- cm (float, iterable(float), RandomDistribution or (mapping) function) – \(C_m\)
- v_rest (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{rest}\)
- v_reset (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{reset}\)
- v_thresh (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{thresh}\)
- tau_syn_E (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_{e_1}\)
- tau_syn_E2 (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_{e_2}\)
- tau_syn_I (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_i\)
- tau_refrac (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{refrac}\)
- i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)
- v (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{init}\)
- isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_{e_1}\)
- isyn_exc2 (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_{e_2}\)
- isyn_inh (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_i\)
- multiplicator (float, iterable(float), RandomDistribution or (mapping) function) –
- exc2_old (float, iterable(float), RandomDistribution or (mapping) function) –
- scaling_factor (float, iterable(float), RandomDistribution or (mapping) function) –